Abstract Wiretap coding, evolving in parallel with cryptography for nearly 50 years, focuses on secure transmission under the assumption that the wiretap channel is no less noisy than the main channel. Most provably secure schemes rely on information-theoretic security but often achieve limited practical rates. This paper proposes a framework for computationally secure modular wiretap coding. We integrate error correction encoding with the channel transition process to define the wiretap channel function, which consists of an invertible function and a lossy function. Secure encoding is then modeled as a computational entropy extractor. A detailed analysis of the lossy function for symmetric wiretap channels is presented. To leverage this lossiness, we design two computational extractors: the invertible fooling extractor (IFE) and the compressed randomness extractor (CRE). For practical implementation, we demonstrate that a 4-round optimal asymmetric encryption padding serves as an IFE in the random oracle model. Experimental comparisons show that our scheme achieves approximately 3 times and 2.7 times the code rates of the Invert-then-Encode and code-based schemes—classical information-theoretic schemes—under equivalent channel conditions. By instantiating IFE and CRE with hash algorithms such as SHAKE-128/256, we develop a practical wiretap coding scheme that achieves high rates with reasonable computational overhead.

This paper proposes an Prediction of Multivariate Spatial-Temporal Series Data Based on Adaptive Spatial-Temporal Information(ASTCN), which learns complex spatio-temporal information from multivariate spatio-temporal series through trainable temporal embeddings and graph adjacency matrices. A gated fusion mechanism is employed to control the proportion of different temporal embeddings to improve prediction accuracy. Visualization of the temporal embeddings reveals that weekly temporal embeddings have the greatest impact on prediction accuracy, followed by daily temporal embeddings, while monthly temporal embeddings have the least impact. Additionally, a novel method for constructing graph adjacency matrices is introduced. Ablation experiments demonstrate that the two types of graph adjacency matrices proposed in this method have varying degrees of influence on improving the prediction accuracy of the dataset. Consequently, this paper integrates the two graph adjacency matrices, enabling ASTCN to achieve superior prediction accuracy on traffic speed, traffic flow, and air quality datasets compared to when either matrix is used alone. In comparative experiments, ASTCN ultimately achieves excellent prediction performance with relatively low training costs.

Abstract The existing physical layer security schemes, which are based on the key generation model and the wire-tap channel model, achieve security by utilizing channel reciprocity entropy and noise entropy, respectively. In contrast, we propose a novel secure transmission framework that combines noise entropy with reciprocity entropy, achieved by inserting reciprocity entropy into the frozen bits of polar codes. Note that in real-world scenarios, when eavesdroppers employ polynomial-time attacks, the bit error rate (BER) increases due to the introduction of computational entropy. To achieve indistinguishability security, we convert the practical physical layer security metric, BER, into the average min-entropy, a widely accepted concept in cryptography. The simulation results demonstrate that the eavesdropper’s BER can be significantly increased without compromising the communication performance of the legitimate receiver. Under concrete parameters we selected, when compared to the joint scheme of physical layer key generation and one time pad, the modular semantically-secure scheme based on the wire-tap channel model, and the simple channel entropy combination scheme, our scheme achieves a message rate approximately 1.2 times, 3.8 times, and 1.4 times better, respectively. Experimental testing validates the feasibility of our scheme.

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.